Load the counts from GSNAP counts
setwd("/Users/stajich/projects/A_fumigatus/Afum_RNASeq_eefA")
countdata <- read.table("reports/Hypoxia.Af293.gsnap_reads.nostrand.tab",
header=TRUE, row.names=1)
colnames(countdata) <- gsub("\\.gsnap_Afum_Af293\\.[sb]am$", "", colnames(countdata))
colnames(countdata) <- gsub("aln\\.", "", colnames(countdata))
countdata <- countdata[ ,6:ncol(countdata)]
countdata <- as.matrix(countdata)
#head(countdata)
samples <- read.csv("samples.csv",header=TRUE)
exprnames <- do.call(paste,c(samples[c("Strain","Condition","Replicate")],sep="_"))
exprnames <- sub(".([123])$",".r\\1",exprnames,perl=TRUE)
# check that experimental columns match in order
all(exprnames %in% colnames(countdata))
## [1] TRUE
all(exprnames == colnames(countdata))
## [1] FALSE
# reorder the columns anyways... in case data change along the way
countdata <- countdata[,exprnames]
all(exprnames == colnames(countdata))
## [1] TRUE
prepare the DESeq objects and analyses
Load a table with the genotype by replicate by treatment information
## [1] 10130
## [1] 9932
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## rlog() may take a few minutes with 30 or more samples,
## vst() is a much faster transformation
## AF293_Normoxia.r1 AF293_Normoxia.r2 AF293_Normoxia.r3
## Afu8g05820 5.541035 5.918401 5.631651
## Afu1g00220 9.524851 9.188179 9.332928
## Afu1g11050 5.194070 5.233264 5.395223
## AF293_Hypoxia.r1 AF293_Hypoxia.r2 AF293_Hypoxia.r3
## Afu8g05820 4.965843 5.235733 5.370264
## Afu1g00220 9.429123 9.305190 9.127422
## Afu1g11050 5.927175 5.378108 5.322468
## Delta_eefA_AF293_Normoxia.r1 Delta_eefA_AF293_Normoxia.r2
## Afu8g05820 5.813727 5.949488
## Afu1g00220 9.624740 9.761400
## Afu1g11050 5.058145 5.156440
## Delta_eefA_AF293_Normoxia.r3 Delta_eefA_AF293_Hypoxia.r1
## Afu8g05820 5.912595 5.004012
## Afu1g00220 9.705158 9.404233
## Afu1g11050 4.973572 5.607735
## Delta_eefA_AF293_Hypoxia.r2 Delta_eefA_AF293_Hypoxia.r3
## Afu8g05820 5.709117 5.725944
## Afu1g00220 9.193827 9.381498
## Afu1g11050 5.434107 6.065524
## eefA_OE_Normoxia.r1 eefA_OE_Normoxia.r2 eefA_OE_Normoxia.r3
## Afu8g05820 5.935578 5.703076 6.176832
## Afu1g00220 8.794052 8.748906 8.686071
## Afu1g11050 5.136203 5.106237 5.022830
## eefA_OE_Hypoxia.r1 eefA_OE_Hypoxia.r2 eefA_OE_Hypoxia.r3
## Afu8g05820 4.956510 4.556240 4.878254
## Afu1g00220 9.357518 9.324668 9.067162
## Afu1g11050 5.695331 4.973537 5.440382
## eefA_REV_Normoxia.r1 eefA_REV_Normoxia.r2 eefA_REV_Normoxia.r3
## Afu8g05820 5.838379 5.743655 5.788968
## Afu1g00220 8.594044 8.553587 8.527232
## Afu1g11050 5.016668 5.126639 4.570710
## eefA_REV_Hypoxia.r1 eefA_REV_Hypoxia.r2 eefA_REV_Hypoxia.r3
## Afu8g05820 4.950019 5.108641 5.679440
## Afu1g00220 9.171863 9.198001 9.060365
## Afu1g11050 5.215491 4.860740 5.359741
## EVOL_Normoxia.r1 EVOL_Normoxia.r2 EVOL_Normoxia.r3
## Afu8g05820 5.706065 5.761864 5.946817
## Afu1g00220 8.661995 8.692220 8.359535
## Afu1g11050 5.317572 5.046636 5.496991
## EVOL_Hypoxia.r1 EVOL_Hypoxia.r2 EVOL_Hypoxia.r3
## Afu8g05820 5.010473 4.620046 5.579350
## Afu1g00220 9.886974 9.848927 9.664264
## Afu1g11050 5.306967 5.381549 5.652347
## AF293_Normoxia.r1 AF293_Normoxia.r2 AF293_Normoxia.r3
## Afu8g05820 5.134334 5.518898 5.227872
## Afu1g00220 9.450351 9.156357 9.282708
## Afu1g11050 4.707125 4.749767 4.923155
## AF293_Hypoxia.r1 AF293_Hypoxia.r2 AF293_Hypoxia.r3
## Afu8g05820 4.519403 4.822512 4.957699
## Afu1g00220 9.366595 9.258235 9.103386
## Afu1g11050 5.463004 4.900635 4.844617
## Delta_eefA_AF293_Normoxia.r1 Delta_eefA_AF293_Normoxia.r2
## Afu8g05820 5.412945 5.549422
## Afu1g00220 9.537580 9.657085
## Afu1g11050 4.557937 4.666003
## Delta_eefA_AF293_Normoxia.r3 Delta_eefA_AF293_Hypoxia.r1
## Afu8g05820 5.512032 4.570053
## Afu1g00220 9.607869 9.344747
## Afu1g11050 4.463115 5.138416
## Delta_eefA_AF293_Hypoxia.r2 Delta_eefA_AF293_Hypoxia.r3
## Afu8g05820 5.300729 5.322912
## Afu1g00220 9.161244 9.325047
## Afu1g11050 4.958216 5.600279
## eefA_OE_Normoxia.r1 eefA_OE_Normoxia.r2 eefA_OE_Normoxia.r3
## Afu8g05820 5.533940 5.299666 5.770554
## Afu1g00220 8.812538 8.773274 8.718402
## Afu1g11050 4.644813 4.612889 4.520274
## eefA_OE_Hypoxia.r1 eefA_OE_Hypoxia.r2 eefA_OE_Hypoxia.r3
## Afu8g05820 4.554732 4.111874 4.446341
## Afu1g00220 9.303584 9.275212 9.050948
## Afu1g11050 5.211151 4.481174 4.965007
## eefA_REV_Normoxia.r1 eefA_REV_Normoxia.r2 eefA_REV_Normoxia.r3
## Afu8g05820 5.436646 5.339726 5.386414
## Afu1g00220 8.638141 8.603246 8.580014
## Afu1g11050 4.513377 4.636187 4.003186
## eefA_REV_Hypoxia.r1 eefA_REV_Hypoxia.r2 eefA_REV_Hypoxia.r3
## Afu8g05820 4.529399 4.701422 5.273547
## Afu1g00220 9.142116 9.164846 9.044947
## Afu1g11050 4.733895 4.375924 4.883589
## EVOL_Normoxia.r1 EVOL_Normoxia.r2 EVOL_Normoxia.r3
## Afu8g05820 5.304344 5.360985 5.546657
## Afu1g00220 8.697143 8.723541 8.433151
## Afu1g11050 4.840706 4.544458 5.030288
## EVOL_Hypoxia.r1 EVOL_Hypoxia.r2 EVOL_Hypoxia.r3
## Afu8g05820 4.587578 4.201434 5.169540
## Afu1g00220 9.766033 9.732401 9.571101
## Afu1g11050 4.827965 4.902749 5.172884
## Warning in design(object) == formula(~1): longer object length is not a
## multiple of shorter object length
## using pre-existing size factors
## estimating dispersions
## found already estimated dispersions, replacing these
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## Warning in design(object) == formula(~1): longer object length is not a
## multiple of shorter object length
## Warning in modelFormula == formula(~1): longer object length is not a
## multiple of shorter object length
## -- replacing outliers and refitting for 41 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)
## estimating dispersions
## fitting model and testing
## Warning in design(object) == formula(~1): longer object length is not a
## multiple of shorter object length
## Warning in design(object) == formula(~1): longer object length is not a
## multiple of shorter object length
## log2 fold change (MLE): condition Normoxia vs Hypoxia
## Wald test p-value: condition Normoxia vs Hypoxia
## DataFrame with 9932 rows and 6 columns
## baseMean log2FoldChange lfcSE
## <numeric> <numeric> <numeric>
## Afu8g05820 35.872486504032 0.769156128518177 0.138658472144817
## Afu1g00220 589.679004620398 -0.32638497706248 0.150495835650241
## Afu1g11050 28.8829103498098 -0.492600607666457 0.14750115957047
## Afu5g13370 158.37999870191 1.77425257689903 0.140757494477956
## Afu6g13800 1474.72793544823 -2.24151096947816 0.102977523044546
## ... ... ... ...
## Afu6g06700 108.212014468083 1.8310188038767 0.139445939351487
## Afu5g13780 59.0024937272377 0.662717147207926 0.123036222348646
## Afu1g10880 413.038759626856 -0.026689439401676 0.219301284101813
## Afu7g06170 19.3130076116073 -1.52262245870866 0.389184420007072
## Afu4g00830 43.4333053553617 -2.03159485229236 0.339042935008594
## stat pvalue padj
## <numeric> <numeric> <numeric>
## Afu8g05820 5.54712681180316 2.90402122905489e-08 5.57670898046658e-08
## Afu1g00220 -2.1687309529349 0.0301031159881272 0.0390115015649894
## Afu1g11050 -3.33963888216832 0.000838873940719821 0.00124279474630508
## Afu5g13370 12.6050309681869 1.98094780582865e-36 1.1034645881935e-35
## Afu6g13800 -21.7669924776548 4.76915132838069e-105 1.94926794211839e-103
## ... ... ... ...
## Afu6g06700 13.1306713726632 2.19700027490564e-39 1.35700290611709e-38
## Afu5g13780 5.38635805421589 7.18996622910886e-08 1.35350160325074e-07
## Afu1g10880 -0.121702157426881 0.903134904561667 0.914366551692811
## Afu7g06170 -3.91234175993219 9.14054311505406e-05 0.000145022163288685
## Afu4g00830 -5.99214625203993 2.07089553101754e-09 4.19929244876811e-09
## log2 fold change (MLE): condition Normoxia vs Hypoxia
## Wald test p-value: condition Normoxia vs Hypoxia
## DataFrame with 9932 rows and 6 columns
## baseMean log2FoldChange lfcSE
## <numeric> <numeric> <numeric>
## Afu8g05820 35.872486504032 0.769156128518177 0.138658472144817
## Afu1g00220 589.679004620398 -0.32638497706248 0.150495835650241
## Afu1g11050 28.8829103498098 -0.492600607666457 0.14750115957047
## Afu5g13370 158.37999870191 1.77425257689903 0.140757494477956
## Afu6g13800 1474.72793544823 -2.24151096947816 0.102977523044546
## ... ... ... ...
## Afu6g06700 108.212014468083 1.8310188038767 0.139445939351487
## Afu5g13780 59.0024937272377 0.662717147207926 0.123036222348646
## Afu1g10880 413.038759626856 -0.026689439401676 0.219301284101813
## Afu7g06170 19.3130076116073 -1.52262245870866 0.389184420007072
## Afu4g00830 43.4333053553617 -2.03159485229236 0.339042935008594
## stat pvalue padj
## <numeric> <numeric> <numeric>
## Afu8g05820 5.54712681180316 2.90402122905489e-08 5.57670898046658e-08
## Afu1g00220 -2.1687309529349 0.0301031159881272 0.0390115015649894
## Afu1g11050 -3.33963888216832 0.000838873940719821 0.00124279474630508
## Afu5g13370 12.6050309681869 1.98094780582865e-36 1.1034645881935e-35
## Afu6g13800 -21.7669924776548 4.76915132838069e-105 1.94926794211839e-103
## ... ... ... ...
## Afu6g06700 13.1306713726632 2.19700027490564e-39 1.35700290611709e-38
## Afu5g13780 5.38635805421589 7.18996622910886e-08 1.35350160325074e-07
## Afu1g10880 -0.121702157426881 0.903134904561667 0.914366551692811
## Afu7g06170 -3.91234175993219 9.14054311505406e-05 0.000145022163288685
## Afu4g00830 -5.99214625203993 2.07089553101754e-09 4.19929244876811e-09
## [1] "Intercept" "condition_Normoxia_vs_Hypoxia"
## using 'apeglm' for LFC shrinkage. If used in published research, please cite:
## Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
## sequence count data: removing the noise and preserving large differences.
## bioRxiv. https://doi.org/10.1101/303255
## log2 fold change (MAP): condition Normoxia vs Hypoxia
## Wald test p-value: condition Normoxia vs Hypoxia
## DataFrame with 9932 rows and 5 columns
## baseMean log2FoldChange lfcSE
## <numeric> <numeric> <numeric>
## Afu8g05820 35.872486504032 0.758403901214331 0.137461142278937
## Afu1g00220 589.679004620398 -0.319493566124052 0.149087465253526
## Afu1g11050 28.8829103498098 -0.483657213971571 0.145469568503771
## Afu5g13370 158.37999870191 1.76092998967783 0.141385716877704
## Afu6g13800 1474.72793544823 -2.23461749424532 0.10305539871214
## ... ... ... ...
## Afu6g06700 108.212014468083 1.8179462541189 0.139983678807195
## Afu5g13780 59.0024937272377 0.654921399819531 0.122570404367868
## Afu1g10880 413.038759626856 -0.0258530863370315 0.214438131526617
## Afu7g06170 19.3130076116073 -1.41760383734365 0.387669123213961
## Afu4g00830 43.4333053553617 -1.95429250176488 0.340710119598636
## pvalue padj
## <numeric> <numeric>
## Afu8g05820 2.90402122905489e-08 5.57670898046658e-08
## Afu1g00220 0.0301031159881272 0.0390115015649894
## Afu1g11050 0.000838873940719821 0.00124279474630508
## Afu5g13370 1.98094780582865e-36 1.1034645881935e-35
## Afu6g13800 4.76915132838069e-105 1.94926794211839e-103
## ... ... ...
## Afu6g06700 2.19700027490564e-39 1.35700290611709e-38
## Afu5g13780 7.18996622910886e-08 1.35350160325074e-07
## Afu1g10880 0.903134904561667 0.914366551692811
## Afu7g06170 9.14054311505406e-05 0.000145022163288685
## Afu4g00830 2.07089553101754e-09 4.19929244876811e-09
##
## out of 9932 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 3767, 38%
## LFC < 0 (down) : 4254, 43%
## outliers [1] : 0, 0%
## low counts [2] : 0, 0%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## out of 9932 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 3768, 38%
## LFC < 0 (down) : 4253, 43%
## outliers [1] : 0, 0%
## low counts [2] : 0, 0%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## out of 9932 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 3663, 37%
## LFC < 0 (down) : 4095, 41%
## outliers [1] : 0, 0%
## low counts [2] : 0, 0%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## [1] 7229
{r pressure, echo=FALSE} #